A real-time anchor-free defect detector with global and local feature enhancement for surface defect detection

Industrial surface defect detection (ISDD) is vital for manufacturing enterprises to control product quality. Many general object detection (GOD) methods are utilized in this field. However, they rarely take into full account the characteristics of industrial defects. We identify three crucial characteristics in ISDD: complex background, small size defect and irregular shape. To copy with it, in this paper, we proposed a novel real-time anchorfree defect detector for ISDD. Firstly, to reduce noise interfere from complex background, we proposed global feature enhancement module (GFEM) to enhance high-level feature's ability in modeling global information so that background noises are alleviated. Secondly, to enhance small size defect's feature, we introduced local feature enhancement module (LFEM). It enhances small size defect's feature by amplifying local peaks in lowlevel features. Thirdly, we introduced box refinement module (BRM) to capture defect's shape information to provide more accurate prediction result. Lastly, we evaluated the proposed defect detector's effectiveness using three public ISDD datasets. The experimental results are promising: our detector achieves a mAP of 92.0% on PVEL_AD, 99.6% on the PCB defect dataset, and 81.6% on NEU-DET. These scores outperform state-of-the-art methods, proving the superiority of our proposed detector. Additionally, it reached 46.1 FPS on the PVEL_AD dataset, showing its capability for real-time detection.